AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods

Ather Abbas , Laurie Boithias (1) , Yakov Pachepsky , Kyunghyun Kim , Jong Ahn Chun , Kyung Hwa Cho

1 GET - Géosciences Environnement Toulouse

Machine learning has shown great promise for simulating hydrological phenomena. However, the development of machine-learning-based hydrological models requires advanced skills from diverse fields, such as programming and hydrological modeling. Additionally, data pre-processing and post-processing when training and testing machine learning models are a time-intensive process. In this study, we developed a python-based framework that simplifies the process of building and training machine-learning-based hydrological models and automates the process of pre-processing hydrological data and post-processing model results. Pre-processing utilities assist in incorporating domain knowledge of hydrology in the machine learning model, such as the distribution of weather data into hydrologic response units (HRUs) based on different HRU discretization definitions. The post-processing utilities help in interpreting the model's results from a hydrological point of view. This framework will help increase the application of machine-learning-based modeling approaches in hydrological sciences.

Domaines: Planète et Univers [physics]

Ather Abbas, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun, et al.. AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods. Geoscientific Model Development, 2022, 15, pp.3021-3039. ⟨10.5194/gmd-15-3021-2022⟩. ⟨insu-03661482⟩